超越Neyman-Pearson:E值可以通过数据驱动的alpha来测试假设
1Machine Learning Group, National research institute for mathematics and computer science in the Netherlands (Centrum Wiskunde & Informatica), Amsterdam 1098 XG, The Netherlands.
概括
该研究介绍了e值作为统计假设测试中P值的优越替代品. 电子值提供了更好的决策,特别是极端数据,并为I型和II型错误提供了强大的风险控制.
科学领域:
- 统计 统计 统计 统计
- 统计学假设测试 统计学假设测试
背景情况:
- 传统的假设测试依赖于P值,P值在决策中具有极端观察的局限性.
- 目前的方法缺乏明确的指导来优化数据观察后的频率决策.
研究的目的:
- 为了证明在统计假设测试中使用e值而不是P值的优势.
- 突出电子价值如何促进后期设置中更好的决策和风险控制.
主要方法:
- 该研究提出并分析了在一个概括的尼曼-皮尔森框架内使用e值的情况.
- 它探讨了基于电子价值的决策规则,用于控制I型和II型风险.
- 这项研究将应用范围扩展到电子信任集和电子后台,以获得有效的风险保证.
主要成果:
- 电子值提供了简单的I型风险控制,并使更好的频率决策成为可能,特别是在极端数据的情况下.
- 在考虑II型风险的后期设置中,基于电子价值的规则是唯一可接受的决策规则.
- 电子信任集和电子后期提供有效的风险担保,当损失函数没有事先确定时.
结论:
- 对于统计假设测试和决策,E值比P值显著提升.
- 它们的应用确保了在各种场景中有效的风险控制,包括事后分析以及当损失函数灵活时.
- 进一步开发和部署电子价值观对于在统计实践中更广泛采用至关重要.
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